
RepoRose
Start new claude chat with full context without using tokens
What it does
One small file. Total context for your AI agent. RepoRose generates structured codebase maps that cut tokens by ~90% and stop your AI from re-reading the repo whenever you start a new chat.
Does the same job
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- RARetain – A unified knowledge base for all your AI coding conversationsJan 2026 · github.com · ▲45
Hey HN! I built Retain as the evolution of claude-reflect (github.com/BayramAnnakov/claude-reflect). The original problem: I use Claude Code/Codex daily for coding, plus claude.ai and ChatGPT occasionally. Every conversation contains decisions, corrections, and patterns I forget existed weeks later. I kept re-explaining the same preferences. claude-reflect was a CLI tool that extracted learnings from Claude Code sessions. Retain takes this further with a native macOS app that: - Aggregates conversations from Claude Code, claude.ai, ChatGPT, and Codex CLI - Instant full-text…
- CRClaude-replay – A video-like player for Claude Code sessionsMar 2026 · github.com · ▲105
I got tired of sharing AI demos with terminal screenshots or screen recordings. Claude Code already stores full session transcripts locally as JSONL files. Those logs contain everything: prompts, tool calls, thinking blocks, and timestamps. I built a small CLI tool that converts those logs into an interactive HTML replay. You can step through the session, jump through the timeline, expand tool calls, and inspect the full conversation. The output is a single self-contained HTML file — no dependencies. You can email it, host it anywhere, embed it in a blog post, and it works on mobile. Repo:…
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- NW
Hey HN, Henry here from Cactus. We open-sourced Needle, a 26M parameter function-calling (tool use) model. It runs at 6000 tok/s prefill and 1200 tok/s decode on consumer devices. We were always frustrated by the little effort made towards building agentic models that run on budget phones, so we conducted investigations that led to an observation: agentic experiences are built upon tool calling, and massive models are overkill for it. Tool calling is fundamentally retrieval-and-assembly (match query to tool name, extract argument values, emit JSON), not reasoning. Cross-attention…
Life & fun · May 2026 · github.com
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Dev tools · May 2026 · github.com